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论文题目: Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection
英文论文题目: Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection
第一作者: Liu, LL; Chen, L; Zhang, YH; Wei, L; Cheng, SW; Kong, XY; Zheng, MY; Huang, T; Cai, YD
英文第一作者: Liu, LL; Chen, L; Zhang, YH; Wei, L; Cheng, SW; Kong, XY; Zheng, MY; Huang, T; Cai, YD
联系作者: Huang, T (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China.
英文联系作者: Huang, T (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China.
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发表年度: 2017
卷: 35
期: 2
页码: 312-329
摘要: Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.
英文摘要: Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.
刊物名称: JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
英文刊物名称: JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
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学科: Biochemistry & Molecular Biology; Biophysics
英文学科: Biochemistry & Molecular Biology; Biophysics
影响因子: 3.123
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论文类别: Article
英文论文类别: Article
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